Data anonymization

The organizations produce and store all kinds of data in their information system every day. The diversity of data brings us to ask what are the best security policies for data, and how to validate a project. For example, data produced can be duplicated in training environments, test environments, critical data, confidential data, and sensitive data for the organization can be found on a protected hosting.

The question of data anonymization is indeed a crucial component in the data governance and cybersecurity approach in order to better anticipate the protection of your data.

The solution that guarantees the confidentiality of your data while maintaining its usability.
MyDataAnonymizer is one of the options we have packaged from our MyDataCatalog platform, to allow you to have a ready-to-use solution to deal with your regulatory and data privacy issues.

The steps of the anonymization process by MyDataAnonymizer
Identifying the data to be anonymized: this involves identifying the data that needs to be anonymized according to current regulations or your security policies.
Determining the anonymization techniques to be used: the substitution mode and choosing the consistency of values (random or constant).
Setting up the anonymization: downloading and running the script on non-production databases.
Validating anonymization: it is important to validate the effectiveness of anonymization by running tests to verify that anonymized data cannot be re-identified.
Anonymization monitoring: data anonymization should be regularly checked and updated as necessary to ensure continued protection and compliance with applicable regulations.

The benefits

Reduce the risk of data leakage.

When personal or identifying data is used outside the production environment, there is a significant risk of data leakage. In order to minimize this risk anonymization helps protect this data in these different contexts.

Ensure data consistency and usability

In effect, anonymization allows you to retain the original format of the data, while hiding personal or identifying information. This allows your teams to work with test data that accurately reflects real-world conditions, without compromising the security and privacy of sensitive data.

Reduce the impact of GDPR-related constraints on the organization's strategy.

Regulations, including the RGPD, require organizations to anonymize or pseudonymize the personal or identifying data they process. These constraints can impact the organization's strategy. To comply more easily with these constraints, Dawizz proposes a data desensitization method.

The search for fields to anonymize is done by the classification of metadata and, or directly by a comprehension of data.